#Guides (AI Translation Handbook)

#Overview

Longer-form guidance for fiction, consistency, offline setups, and how AI-assisted translation compares to classic CAT tooling. These guides are editorial — they explain the why behind the methods, then point at the concrete CatScribe workflows that implement them.

• Translate Novels With AI — what actually goes wrong when machines translate fiction, and the editorial pipeline that fixes it: glossary-first setup, chapter batching, refinement layers, and human review.
• AI Translation Consistency — why models drift on long texts and how layered enforcement (term shielding, prompt constraints, post-verification, QA reporting) keeps terminology stable.
• Offline AI Translation — the honest case for local translation: privacy and predictability, what "offline" really requires on first run, and which local engines fit which jobs.
• AI vs Traditional CAT Tools — how classic CAT concepts (segments, translation memory, termbases, QA checks) map onto an AI-assisted workflow, and where each approach wins.

#How To Use These Guides

For hands-on, screen-by-screen instructions, pair each guide with its workflow counterpart: Translating Books, Maintaining Consistency, Reviewing Translations, and the feature references under Features.